Project Summary/Abstract Healthcare-associated infections (HAIs) are a major source of mortality and morbidity. Within hospitals, infections like in?uenza, methicillin-resistant Staphylococcus aureus (MRSA), and antimicrobial-resistant Gram- negative infections routinely spread to and among hospitalized patients: HAIs affect about two million patients in American hospitals each year. Of particular concern are strains of antimicrobial resistant pathogens that could also be quickly ampli?ed in hospitals, transmitted to other hospitals, nursing homes, and then, eventually, to the community at large. Unfortunately, much of what we know about infectious diseases and the mechanics of disease transmission is based on observational data rather than carefully controlled experimental evidence. Disease detection most often relies upon clinical testing, which is not strictly needed from a patient care perspective and is therefore rarely performed at scale. Experiments to estimate how a disease may behave in an infected individual are ethically challenging. And while randomized controlled trials that test the effectiveness of interventions (e.g., gowns and gloves, environmental cleaning, etc.) are certainly possible, they are expensive and their results are often dif?cult to generalize and abstract to different contexts. Yet developing effective interventions to attenuate the spread of HAIs remains an important public health goal, and demands some means by which the effectiveness of proposed interventions (or combinations thereof) can be ef?ciently and inexpensively compared. In ?elds where experiments are not possible, mathematical models and computer simulations can yield insight into how a system behaves under pressure from external forces, such as the intervention under study. The overarching theme of this proposal is that high-?delity models derived from existing, complex, ?ne- grained data can be used to simulate the spread of disease in healthcare facilities, compare the effectiveness of alternative interventions, and support public health of?cials as they make resource allocation decisions. The expected outcome of this project is a much-needed framework for the evaluation and comparison of hospital patient safety measures, from common interventions speci?cally designed for infection control, such as handwashing or patient cohorting, targeted handwashing compliance measures, and healthcare worker vaccination strategies, to more subtle questions about, e.g., patient/unit assignment, within- and inter-hospital transfer policies, antibiotic administration and staff/patient allocations. The broader impact of this project is a computational science methodology that can elucidate, in an extremely cost-effective manner, the subtle downstream effects of complex interactions between routine operational policies and procedures on the spread of infections within and across healthcare institutions.